table(dataAll$Session, dataAll$Drug)
MPH PBO SUL
1 31 37 22
2 28 29 33
3 31 24 35
# turn drug conditions into factor levels
dataAll$Drug <- factor(dataAll$Drug, levels = c("MPH","SUL","PBO"));
dataAll$PutamenSplit <- factor(dataAll$PutamenSplit, levels = c("0","1"));
dataAll$CaudateSplit <- factor(dataAll$CaudateSplit, levels = c("0","1"));
dataAll$VSSplit <- factor(dataAll$VSSplit, levels = c("0","1"));
dataAll$DifEnd_Total <- dataAll$DifferentEnd/dataAll$Total # divide different end scores by total number of ideas
dataAll$DifEnd_Con <- dataAll$DifferentEnd/dataAll$Convergent_Pasta # divide different end scores by total number of ideas
# set contrasts to sum-to-zero
options(contrasts=c("contr.sum", "contr.poly"))
# set two dataframes for the contrast between MPH and PBO - between SUL and PBO
df_MPH <- dplyr::filter(dataAll, Drug %in% c("MPH","PBO"))
df_SUL <- dplyr::filter(dataAll, Drug %in% c("SUL","PBO"))
data_PBO <- dplyr::filter(dataAll, Drug %in% c("PBO"))
print(summary(Placebo_PASTA_DE_Caudate), corr=F) # print summary without fixed effect correlation matrix
Call:
lm(formula = DifferentEnd ~ 1 + Caudate_ki.c, data = data_PBO,
REML = F)
Residuals:
Min 1Q Median 3Q Max
-5.8421 -1.8324 -0.2242 1.5334 6.9476
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 7.6444 0.2932 26.076 <2e-16 ***
Caudate_ki.c -0.3340 0.2948 -1.133 0.26
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 2.781 on 88 degrees of freedom
Multiple R-squared: 0.01438, Adjusted R-squared: 0.003176
F-statistic: 1.284 on 1 and 88 DF, p-value: 0.2603
print(summary(Placebo_PASTA_DE_VS), corr=F) # print summary without fixed effect correlation matrix
Call:
lm(formula = DifferentEnd ~ 1 + VS_ki.c, data = data_PBO, REML = F)
Residuals:
Min 1Q Median 3Q Max
-6.1984 -1.7955 -0.1105 1.4734 7.0567
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 7.6444 0.2931 26.081 <2e-16 ***
VS_ki.c -0.3388 0.2947 -1.149 0.253
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 2.781 on 88 degrees of freedom
Multiple R-squared: 0.01479, Adjusted R-squared: 0.003597
F-statistic: 1.321 on 1 and 88 DF, p-value: 0.2535
print(summary(Placebo_PASTA_DE_Putamen), corr=F) # print summary without fixed effect correlation matrix
Call:
lm(formula = DifferentEnd ~ 1 + Putamen_ki.c, data = data_PBO,
REML = F)
Residuals:
Min 1Q Median 3Q Max
-5.907 -1.685 -0.222 1.583 7.296
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 7.6444 0.2902 26.340 <2e-16 ***
Putamen_ki.c -0.5142 0.2918 -1.762 0.0816 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 2.753 on 88 degrees of freedom
Multiple R-squared: 0.03407, Adjusted R-squared: 0.0231
F-statistic: 3.104 on 1 and 88 DF, p-value: 0.08157
##################### SUL #####################################################
##################### SUL #####################################################
##################### SUL #####################################################
##################### SUL #####################################################
##################### SUL #####################################################
SUL_Caudate_RAT <- lmer(Convergent_RAT ~ 1 + Drug*Caudate_ki.c + Session + (1 | ID), data = df_SUL, REML=F)
print(summary(SUL_Caudate_RAT), corr=F) # print summary without fixed effect correlation matrix
Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: Convergent_RAT ~ 1 + Drug * Caudate_ki.c + Session + (1 | ID)
Data: df_SUL
AIC BIC logLik deviance df.resid
682.0 704.4 -334.0 668.0 173
Scaled residuals:
Min 1Q Median 3Q Max
-2.33866 -0.61144 -0.09998 0.54320 2.60092
Random effects:
Groups Name Variance Std.Dev.
ID (Intercept) 0.2652 0.515
Residual 2.1444 1.464
Number of obs: 180, groups: ID, 90
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 4.42458 0.30755 164.74835 14.387 <2e-16 ***
Drug1 0.15409 0.11104 90.69284 1.388 0.1686
Caudate_ki.c -0.03719 0.12278 89.75565 -0.303 0.7626
Session 0.27938 0.14118 138.33452 1.979 0.0498 *
Drug1:Caudate_ki.c 0.02673 0.10984 89.35964 0.243 0.8083
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
plot_model(SUL_Caudate_RAT, type = "pred", terms = c("Caudate_ki.c","Session","Drug"))
Error in plot_model(SUL_Caudate_RAT, type = "pred", terms = c("Caudate_ki.c", :
could not find function "plot_model"




##################### MPH #####################################################
##################### MPH #####################################################
##################### MPH #####################################################
##################### MPH #####################################################
##################### MPH #####################################################
##################### RAT #####################################################
# MPH*Caudate interaction in predicting Convergent RAT
MPH_Caudate_RAT <- lmer(Convergent_RAT ~ 1 + Drug*Caudate_ki.c + Session + (1 | ID), data = df_MPH, REML=F)
print(summary(MPH_Caudate_RAT), corr=F) # print summary without fixed effect correlation matrix
Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: Convergent_RAT ~ 1 + Drug * Caudate_ki.c + Session + (1 | ID)
Data: df_MPH
AIC BIC logLik deviance df.resid
699.2 721.6 -342.6 685.2 173
Scaled residuals:
Min 1Q Median 3Q Max
-2.25533 -0.59926 -0.01159 0.62992 2.13551
Random effects:
Groups Name Variance Std.Dev.
ID (Intercept) 0.4705 0.6859
Residual 2.2064 1.4854
Number of obs: 180, groups: ID, 90
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 4.42327 0.30110 161.40947 14.690 <2e-16 ***
Drug1 0.04512 0.11118 90.09481 0.406 0.686
Caudate_ki.c -0.11355 0.13309 90.10202 -0.853 0.396
Session 0.22136 0.14032 125.40200 1.577 0.117
Drug1:Caudate_ki.c -0.05078 0.11165 90.01252 -0.455 0.650
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
plot_model(MPH_Caudate_RAT, type = "pred", terms = c("Caudate_ki.c","Session","Drug"))

plot(MPH_Caudate_RAT)

qqnorm(residuals(MPH_Caudate_RAT))

# MPH*Putamen interaction in predicting Convergent RAT
MPH_Putamen_RAT <- lmer(Convergent_RAT ~ 1 + Drug*Putamen_ki.c + Session + (1 | ID), data = df_MPH, REML=F)
print(summary(MPH_Putamen_RAT), corr=F) # print summary without fixed effect correlation matrix
Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: Convergent_RAT ~ 1 + Drug * Putamen_ki.c + Session + (1 | ID)
Data: df_MPH
AIC BIC logLik deviance df.resid
700.0 722.4 -343.0 686.0 173
Scaled residuals:
Min 1Q Median 3Q Max
-2.23723 -0.56564 0.01528 0.64409 2.14803
Random effects:
Groups Name Variance Std.Dev.
ID (Intercept) 0.4789 0.6921
Residual 2.2107 1.4868
Number of obs: 180, groups: ID, 90
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 4.408e+00 3.046e-01 1.615e+02 14.472 <2e-16 ***
Drug1 4.457e-02 1.113e-01 9.012e+01 0.400 0.690
Putamen_ki.c -5.166e-02 1.335e-01 9.008e+01 -0.387 0.700
Session 2.291e-01 1.422e-01 1.263e+02 1.611 0.110
Drug1:Putamen_ki.c -6.206e-04 1.131e-01 9.079e+01 -0.005 0.996
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
plot_model(MPH_Putamen_RAT, type = "pred", terms = c("Putamen_ki.c","Session","Drug"))

plot(MPH_Putamen_RAT)

qqnorm(residuals(MPH_Putamen_RAT))

# Sulpiride*VS interaction in predicting Convergent RAT
MPH_VS_RAT <- lmer(Convergent_RAT ~ 1 + Drug*VS_ki.c + Session + (1 | ID), data = df_MPH, REML=F)
print(summary(MPH_VS_RAT), corr=F) # print summary without fixed effect correlation matrix
Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: Convergent_RAT ~ 1 + Drug * VS_ki.c + Session + (1 | ID)
Data: df_MPH
AIC BIC logLik deviance df.resid
700.1 722.5 -343.1 686.1 173
Scaled residuals:
Min 1Q Median 3Q Max
-2.25335 -0.57046 0.01357 0.63473 2.18173
Random effects:
Groups Name Variance Std.Dev.
ID (Intercept) 0.4812 0.6937
Residual 2.2104 1.4868
Number of obs: 180, groups: ID, 90
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 4.404675 0.304727 161.193156 14.454 <2e-16 ***
Drug1 0.044427 0.111291 90.117143 0.399 0.691
VS_ki.c -0.024468 0.133742 90.366488 -0.183 0.855
Session 0.231004 0.142280 125.810262 1.624 0.107
Drug1:VS_ki.c 0.005332 0.113054 90.738007 0.047 0.962
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
plot_model(MPH_VS_RAT, type = "pred", terms = c("VS_ki.c","Session","Drug"))

plot(MPH_VS_RAT)

qqnorm(residuals(MPH_VS_RAT))

##################### AUT #####################################################
# MPH*Caudate interaction in predicting divergent AUT
MPH_Caudate_AUT <- lmer(AUT_divergent ~ 1 + Drug*Caudate_ki.c + Session + (1 | ID), data = df_MPH, REML=F)
print(summary(MPH_Caudate_AUT), corr=F) # print summary without fixed effect correlation matrix
Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: AUT_divergent ~ 1 + Drug * Caudate_ki.c + Session + (1 | ID)
Data: df_MPH
AIC BIC logLik deviance df.resid
368.5 390.8 -177.2 354.5 173
Scaled residuals:
Min 1Q Median 3Q Max
-2.00602 -0.47336 -0.00095 0.51974 2.24790
Random effects:
Groups Name Variance Std.Dev.
ID (Intercept) 0.4739 0.6884
Residual 0.1590 0.3988
Number of obs: 180, groups: ID, 90
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 4.579e-02 1.107e-01 1.797e+02 0.414 0.680
Drug1 5.518e-02 2.987e-02 9.004e+01 1.847 0.068 .
Caudate_ki.c 2.057e-02 7.887e-02 9.004e+01 0.261 0.795
Session 5.515e-03 4.052e-02 9.755e+01 0.136 0.892
Drug1:Caudate_ki.c 4.754e-02 2.999e-02 9.002e+01 1.585 0.116
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
plot_model(MPH_Caudate_AUT, type = "pred", terms = c("Caudate_ki.c","Session","Drug"))

plot(MPH_Caudate_AUT)

qqnorm(residuals(MPH_Caudate_AUT))

# MPH*Putamen interaction in predicting divergent AUT
MPH_Putamen_AUT <- lmer(AUT_divergent ~ 1 + Drug*Putamen_ki.c + Session + (1 | ID), data = df_MPH, REML=F)
print(summary(MPH_Putamen_AUT), corr=F) # print summary without fixed effect correlation matrix
Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: AUT_divergent ~ 1 + Drug * Putamen_ki.c + Session + (1 | ID)
Data: df_MPH
AIC BIC logLik deviance df.resid
370.7 393.1 -178.4 356.7 173
Scaled residuals:
Min 1Q Median 3Q Max
-1.9656 -0.5025 -0.0477 0.6113 2.2598
Random effects:
Groups Name Variance Std.Dev.
ID (Intercept) 0.4712 0.6864
Residual 0.1633 0.4041
Number of obs: 180, groups: ID, 90
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 5.033e-02 1.122e-01 1.799e+02 0.449 0.6541
Drug1 5.535e-02 3.027e-02 9.005e+01 1.829 0.0707 .
Putamen_ki.c 3.139e-02 7.883e-02 9.003e+01 0.398 0.6914
Session 3.157e-03 4.162e-02 9.800e+01 0.076 0.9397
Drug1:Putamen_ki.c 1.148e-02 3.082e-02 9.023e+01 0.372 0.7105
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
plot_model(MPH_Putamen_AUT, type = "pred", terms = c("Putamen_ki.c","Session","Drug"))

plot(MPH_Putamen_AUT)

qqnorm(residuals(MPH_Putamen_AUT))

# MPH*VS interaction in predicting divergent AUT
MPH_VS_AUT <- lmer(AUT_divergent ~ 1 + Drug*VS_ki.c + Session + (1 | ID), data = df_MPH, REML=F)
print(summary(MPH_VS_AUT), corr=F) # print summary without fixed effect correlation matrix
Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: AUT_divergent ~ 1 + Drug * VS_ki.c + Session + (1 | ID)
Data: df_MPH
AIC BIC logLik deviance df.resid
367.1 389.4 -176.5 353.1 173
Scaled residuals:
Min 1Q Median 3Q Max
-2.0657 -0.4976 -0.0212 0.4985 2.2960
Random effects:
Groups Name Variance Std.Dev.
ID (Intercept) 0.4719 0.6870
Residual 0.1574 0.3967
Number of obs: 180, groups: ID, 90
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 0.02800 0.11102 179.79119 0.252 0.8012
Drug1 0.05451 0.02972 90.04447 1.835 0.0699 .
VS_ki.c 0.06058 0.07869 90.11340 0.770 0.4434
Session 0.01474 0.04087 97.64910 0.361 0.7191
Drug1:VS_ki.c 0.05614 0.03023 90.21370 1.857 0.0666 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
plot_model(MPH_VS_AUT, type = "pred", terms = c("VS_ki.c","Session","Drug"))

plot(MPH_VS_AUT)

qqnorm(residuals(MPH_VS_AUT))



print(summary(MPH_VS_PastDif), corr=F) # print summary without fixed effect correlation matrix
Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: DifferentEnd ~ 1 + Drug * VS_ki.c + Session + (1 | ID)
Data: df_MPH
AIC BIC logLik deviance df.resid
884.6 907.0 -435.3 870.6 173
Scaled residuals:
Min 1Q Median 3Q Max
-1.92989 -0.56982 0.01076 0.51494 2.57408
Random effects:
Groups Name Variance Std.Dev.
ID (Intercept) 4.248 2.061
Residual 4.269 2.066
Number of obs: 180, groups: ID, 90
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 8.18896 0.48011 168.67778 17.056 <2e-16 ***
Drug1 0.21825 0.15472 90.06895 1.411 0.162
VS_ki.c -0.05445 0.26803 90.21373 -0.203 0.839
Session -0.17583 0.20723 107.73944 -0.848 0.398
Drug1:VS_ki.c 0.27050 0.15733 90.42631 1.719 0.089 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Session1 <- dataAll[grep("1", dataAll$Session),]
ggscatter(Session1, x = "Caudate_ki", y = "KDOCStotal",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.method = "pearson",
xlab = "CaudateKi)", ylab = "SubjectiveCreativity")

ggscatter(Session1, x = "Putamen_ki", y = "KDOCStotal",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.method = "pearson",
xlab = "CaudateKi)", ylab = "SubjectiveCreativity")

ggscatter(Session1, x = "VS_ki", y = "KDOCStotal",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.method = "pearson",
xlab = "CaudateKi)", ylab = "SubjectiveCreativity")

NA
NA
# Ecological validity
ConCr <- lmer(Convergent_Pasta ~ 1 + KDOCStotal + Session + (1 | ID), data = dataAll, REML=F)
print(summary(ConCr), corr=F) # print summary without fixed effect correlation matrix
Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: Convergent_Pasta ~ 1 + KDOCStotal + Session + (1 | ID)
Data: dataAll
AIC BIC logLik deviance df.resid
2064.6 2082.6 -1027.3 2054.6 265
Scaled residuals:
Min 1Q Median 3Q Max
-3.3462 -0.4448 -0.0436 0.4098 5.6155
Random effects:
Groups Name Variance Std.Dev.
ID (Intercept) 176.73 13.29
Residual 53.15 7.29
Number of obs: 270, groups: ID, 90
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 22.1557 10.1823 92.0799 2.176 0.0321 *
KDOCStotal -1.5530 3.3546 90.0000 -0.463 0.6445
Session 2.6667 0.5434 180.0000 4.908 2.06e-06 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
DivArt <- lmer(Divergent_Pasta ~ 1 + KDOCStotal + Session + (1 | ID), data = dataAll, REML=F)
print(summary(DivArt), corr=F) # print summary without fixed effect correlation matrix
Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: Divergent_Pasta ~ 1 + KDOCStotal + Session + (1 | ID)
Data: dataAll
AIC BIC logLik deviance df.resid
1791.9 1809.9 -891.0 1781.9 265
Scaled residuals:
Min 1Q Median 3Q Max
-2.1612 -0.5627 -0.1337 0.4537 3.4865
Random effects:
Groups Name Variance Std.Dev.
ID (Intercept) 31.93 5.650
Residual 25.62 5.061
Number of obs: 270, groups: ID, 90
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) -3.9329 4.6797 94.8342 -0.840 0.40279
KDOCStotal 5.1473 1.5303 90.0000 3.364 0.00113 **
Session -0.5389 0.3772 180.0000 -1.428 0.15489
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
DifEnd <- lmer(DifferentEnd ~ 1 + KDOCStotal + Session + (1 | ID), data = dataAll, REML=F)
print(summary(DifEnd), corr=F) # print summary without fixed effect correlation matrix
Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: DifferentEnd ~ 1 + KDOCStotal + Session + (1 | ID)
Data: dataAll
AIC BIC logLik deviance df.resid
1303.5 1321.5 -646.7 1293.5 265
Scaled residuals:
Min 1Q Median 3Q Max
-2.20137 -0.60390 -0.02339 0.60521 2.70988
Random effects:
Groups Name Variance Std.Dev.
ID (Intercept) 3.185 1.785
Residual 4.918 2.218
Number of obs: 270, groups: ID, 90
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 3.6550 1.6286 97.8120 2.244 0.0271 *
KDOCStotal 1.5358 0.5284 90.0000 2.906 0.0046 **
Session -0.2278 0.1653 180.0000 -1.378 0.1699
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
ConCr <- lmer(Convergent_RAT ~ 1 + KDOCStotal + Session + (1 | ID), data = dataAll, REML=F)
print(summary(ConCr), corr=F) # print summary without fixed effect correlation matrix
Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: Convergent_RAT ~ 1 + KDOCStotal + Session + (1 | ID)
Data: dataAll
AIC BIC logLik deviance df.resid
1025.2 1043.2 -507.6 1015.2 265
Scaled residuals:
Min 1Q Median 3Q Max
-2.41132 -0.54539 0.01383 0.59204 2.58679
Random effects:
Groups Name Variance Std.Dev.
ID (Intercept) 0.3869 0.622
Residual 2.1815 1.477
Number of obs: 270, groups: ID, 90
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 4.36595 0.79732 105.11399 5.476 2.99e-07 ***
KDOCStotal 0.09079 0.25392 90.00000 0.358 0.722
Session 0.16111 0.11009 180.00000 1.463 0.145
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
ConCr <- lmer(AUT_divergent ~ 1 + KDOCStotal + Session + (1 | ID), data = dataAll, REML=F)
print(summary(ConCr), corr=F) # print summary without fixed effect correlation matrix
Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: AUT_divergent ~ 1 + KDOCStotal + Session + (1 | ID)
Data: dataAll
AIC BIC logLik deviance df.resid
494.3 512.3 -242.2 484.3 265
Scaled residuals:
Min 1Q Median 3Q Max
-2.25337 -0.50593 -0.04141 0.52584 2.91320
Random effects:
Groups Name Variance Std.Dev.
ID (Intercept) 0.3856 0.621
Residual 0.1806 0.425
Number of obs: 270, groups: ID, 90
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) -1.391632 0.488905 93.086240 -2.846 0.00544 **
KDOCStotal 0.468811 0.160632 89.999988 2.919 0.00444 **
Session 0.001846 0.031674 180.000003 0.058 0.95360
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
## criterion validity
ConDivPasta <- lmer(Convergent_Pasta ~ 1 + Divergent_Pasta + Session + (1 | ID), data = dataAll, REML=F)
print(summary(ConDivPasta), corr=F) # print summary without fixed effect correlation matrix
Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: Convergent_Pasta ~ 1 + Divergent_Pasta + Session + (1 | ID)
Data: dataAll
AIC BIC logLik deviance df.resid
2033.7 2051.7 -1011.9 2023.7 265
Scaled residuals:
Min 1Q Median 3Q Max
-3.1062 -0.4322 -0.0288 0.3897 5.1241
Random effects:
Groups Name Variance Std.Dev.
ID (Intercept) 174.96 13.23
Residual 45.29 6.73
Number of obs: 270, groups: ID, 90
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 23.48101 2.04408 222.87401 11.487 < 2e-16 ***
Divergent_Pasta -0.52133 0.09003 242.49652 -5.791 2.16e-08 ***
Session 2.38573 0.50394 180.39468 4.734 4.43e-06 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
ConDifPasta <- lmer(Convergent_Pasta ~ 1 + DifferentEnd + Session + (1 | ID), data = dataAll, REML=F)
print(summary(ConDifPasta), corr=F) # print summary without fixed effect correlation matrix
Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: Convergent_Pasta ~ 1 + DifferentEnd + Session + (1 | ID)
Data: dataAll
AIC BIC logLik deviance df.resid
2047.3 2065.3 -1018.7 2037.3 265
Scaled residuals:
Min 1Q Median 3Q Max
-3.3542 -0.4773 -0.0304 0.4272 5.5473
Random effects:
Groups Name Variance Std.Dev.
ID (Intercept) 163.85 12.80
Residual 50.12 7.08
Number of obs: 270, groups: ID, 90
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 25.2852 2.5429 267.6428 9.944 < 2e-16 ***
DifferentEnd -0.9425 0.2220 227.1239 -4.246 3.18e-05 ***
Session 2.4520 0.5301 179.7783 4.626 7.12e-06 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
## construct validity
ConDifRAT <- lmer(Convergent_RAT ~ 1 + DifferentEnd + Session + (1 | ID), data = dataAll, REML=F)
print(summary(ConDifRAT), corr=F) # print summary without fixed effect correlation matrix
Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: Convergent_RAT ~ 1 + DifferentEnd + Session + (1 | ID)
Data: dataAll
AIC BIC logLik deviance df.resid
1017.1 1035.1 -503.6 1007.1 265
Scaled residuals:
Min 1Q Median 3Q Max
-2.59175 -0.55540 -0.00063 0.60047 2.57462
Random effects:
Groups Name Variance Std.Dev.
ID (Intercept) 0.3507 0.5922
Residual 2.1353 1.4613
Number of obs: 270, groups: ID, 90
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 5.45017 0.37158 267.19265 14.668 < 2e-16 ***
DifferentEnd -0.09867 0.03407 233.80181 -2.896 0.00413 **
Session 0.13864 0.10919 180.94311 1.270 0.20585
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
ConPastaRAT <- lmer(Convergent_RAT ~ 1 + Convergent_Pasta + Session + (1 | ID), data = dataAll, REML=F)
print(summary(ConPastaRAT), corr=F) # print summary without fixed effect correlation matrix
Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: Convergent_RAT ~ 1 + Convergent_Pasta + Session + (1 | ID)
Data: dataAll
AIC BIC logLik deviance df.resid
1022.2 1040.2 -506.1 1012.2 265
Scaled residuals:
Min 1Q Median 3Q Max
-2.39512 -0.55240 -0.00362 0.58350 2.69559
Random effects:
Groups Name Variance Std.Dev.
ID (Intercept) 0.3728 0.6106
Residual 2.1641 1.4711
Number of obs: 270, groups: ID, 90
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 4.418e+00 2.743e-01 2.697e+02 16.104 <2e-16 ***
Convergent_Pasta 1.252e-02 6.992e-03 1.410e+02 1.790 0.0755 .
Session 1.277e-01 1.112e-01 1.879e+02 1.148 0.2523
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
DivPastaAUT <- lmer(AUT_divergent ~ 1 + Divergent_Pasta + Session + (1 | ID), data = dataAll, REML=F)
print(summary(DivPastaAUT), corr=F) # print summary without fixed effect correlation matrix
Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: AUT_divergent ~ 1 + Divergent_Pasta + Session + (1 | ID)
Data: dataAll
AIC BIC logLik deviance df.resid
487.9 505.9 -239.0 477.9 265
Scaled residuals:
Min 1Q Median 3Q Max
-2.19854 -0.50547 -0.08302 0.51198 2.71362
Random effects:
Groups Name Variance Std.Dev.
ID (Intercept) 0.4273 0.6537
Residual 0.1674 0.4092
Number of obs: 270, groups: ID, 90
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) -0.225507 0.112783 243.280533 -1.999 0.046668 *
Divergent_Pasta 0.020438 0.005269 258.820938 3.879 0.000133 ***
Session 0.012859 0.030631 180.499758 0.420 0.675125
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
DifPastaAUT <- lmer(AUT_divergent ~ 1 + DifferentEnd + Session + (1 | ID), data = dataAll, REML=F)
print(summary(DifPastaAUT), corr=F) # print summary without fixed effect correlation matrix
Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: AUT_divergent ~ 1 + DifferentEnd + Session + (1 | ID)
Data: dataAll
AIC BIC logLik deviance df.resid
500.1 518.1 -245.1 490.1 265
Scaled residuals:
Min 1Q Median 3Q Max
-2.17369 -0.49164 -0.04547 0.50380 2.86086
Random effects:
Groups Name Variance Std.Dev.
ID (Intercept) 0.4273 0.6537
Residual 0.1785 0.4225
Number of obs: 270, groups: ID, 90
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) -0.156433 0.144270 269.655448 -1.084 0.279
DifferentEnd 0.019982 0.012979 239.260058 1.540 0.125
Session 0.006397 0.031626 180.512518 0.202 0.840
ConDivRATAUT <- lmer(Convergent_RAT ~ 1 + AUT_divergent + Session + (1 | ID), data = dataAll, REML=F)
print(summary(ConDivRATAUT), corr=F) # print summary without fixed effect correlation matrix
Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: Convergent_RAT ~ 1 + AUT_divergent + Session + (1 | ID)
Data: dataAll
AIC BIC logLik deviance df.resid
1022.4 1040.4 -506.2 1012.4 265
Scaled residuals:
Min 1Q Median 3Q Max
-2.3473 -0.5412 0.0284 0.5366 2.5965
Random effects:
Groups Name Variance Std.Dev.
ID (Intercept) 0.3973 0.6303
Residual 2.1485 1.4658
Number of obs: 270, groups: ID, 90
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 4.6351 0.2452 251.4018 18.904 <2e-16 ***
AUT_divergent 0.2317 0.1354 159.7671 1.712 0.0888 .
Session 0.1607 0.1093 179.3515 1.471 0.1431
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
if (!require(remotes)) {
install.packages("remotes")
}
Loading required package: remotes
remotes::install_github('jorvlan/raincloudplots')
Downloading GitHub repo jorvlan/raincloudplots@HEAD
These packages have more recent versions available.
It is recommended to update all of them.
Which would you like to update?
1: All
2: CRAN packages only
3: None
4: cli (3.4.1 -> 3.6.0) [CRAN]
5: utf8 (1.2.2 -> 1.2.3) [CRAN]
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ggplot2 (3.4.0 -> 3.4.1) [CRAN]
gghalves (0.1.3 -> 0.1.4) [CRAN]
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checking for file ‘/private/var/folders/q1/3b7vx_bx4cx1q8w19vj870ym0000gp/T/RtmpC4vn3s/remotesb824b759ab/jorvlan-raincloudplots-e5530fc/DESCRIPTION’ ...
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* installing *source* package ‘raincloudplots’ ...
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* DONE (raincloudplots)
library(raincloudplots)

dataSkipped <- lmer(SkippedItems ~ 1 + Session + (1 | subject), data = dataSkippedRAT, REML=F)
print(summary(dataSkipped), corr=F) # print summary without fixed effect correlation matrix
Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: SkippedItems ~ 1 + Session + (1 | subject)
Data: dataSkippedRAT
AIC BIC logLik deviance df.resid
1063.9 1078.6 -528.0 1055.9 284
Scaled residuals:
Min 1Q Median 3Q Max
-2.0238 -0.5618 -0.2190 0.3190 5.7249
Random effects:
Groups Name Variance Std.Dev.
subject (Intercept) 0.5212 0.722
Residual 1.8702 1.368
Number of obs: 288, groups: subject, 96
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 1.05903 0.22558 275.82981 4.695 4.21e-06 ***
Session 0.03646 0.09869 192.00000 0.369 0.712
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1


M = cor(newdata,use="pairwise.complete.obs")
testRes = cor.mtest(newdata, conf.level = 0.95)
corrplot(M, method = 'number') # colorful number
## leave blank on non-significant coefficient
## add significant correlation coefficients
corrplot(M, p.mat = testRes$p, method = 'circle', type = 'lower', insig='blank',
addCoef.col ='black', number.cex = 0.8, diag=FALSE)
M = cor(Avdata,use="pairwise.complete.obs")
testRes = cor.mtest(Avdata, conf.level = 0.95)
corrplot(M, method = 'number') # colorful number

## leave blank on non-significant coefficient
## add significant correlation coefficients
corrplot(M, p.mat = testRes$p, method = 'circle', type = 'lower', insig='blank',
addCoef.col ='black', number.cex = 0.8, diag=FALSE)

data_MPH <- dplyr::filter(dataAll, Drug %in% c("MPH"))
data_MPH <- data_MPH[order(data_MPH$ID),]
data_SUL <- dplyr::filter(dataAll, Drug %in% c("SUL"))
data_SUL <- data_SUL[order(data_SUL$ID),]
data_PBO <- dplyr::filter(dataAll, Drug %in% c("PBO"))
data_PBO <- data_PBO[order(data_PBO$ID),]
df_Correlations <- data.frame(matrix(ncol = 8, nrow = 93))
x <- c("ID", "MPH_PBO_Redo_Av", "MPH_PBO_IP_Av","MPH_PBO_AUT_divergent","MPH_PBO_Divergent_Pasta","MPH_PBO_DifferentEnd","MPH_PBO_Convergent_RAT","MPH_PBO_Convergent_PASTA")
colnames(df_Correlations) <- x
df_Correlations$ID <- data_MPH$ID
Error in `$<-.data.frame`(`*tmp*`, ID, value = c(1L, 2L, 3L, 4L, 5L, 6L, :
replacement has 90 rows, data has 93
# Total number of ideas in PASTA
MPH_Caudate_Total <- lmer(Total ~ 1 + Drug*Caudate_ki.c + Session + (1 | ID), data = df_MPH,REML=F)
print(summary(MPH_Caudate_Total), corr=F) # print summary without fixed effect correlation matrix
Linear mixed model fit by maximum likelihood ['lmerMod']
Formula: Total ~ 1 + Drug * Caudate_ki.c + Session + (1 | ID)
Data: df_MPH
AIC BIC logLik deviance df.resid
1428.8 1451.2 -707.4 1414.8 173
Scaled residuals:
Min 1Q Median 3Q Max
-2.7057 -0.4753 -0.0043 0.3759 3.5178
Random effects:
Groups Name Variance Std.Dev.
ID (Intercept) 176.4 13.281
Residual 56.3 7.504
Number of obs: 180, groups: ID, 90
Fixed effects:
Estimate Std. Error t value
(Intercept) 30.4527 2.1064 14.457
Drug1 1.3418 0.5620 2.388
Caudate_ki.c -0.4490 1.5163 -0.296
Session 1.5750 0.7631 2.064
Drug1:Caudate_ki.c 0.9871 0.5643 1.749
MPH_Putamen_Total <- lmer(Total ~ 1 + Drug*Putamen_ki.c + Session + (1 | ID), data = df_MPH,REML=F)
print(summary(MPH_Putamen_Total), corr=F) # print summary without fixed effect correlation matrix
Linear mixed model fit by maximum likelihood ['lmerMod']
Formula: Total ~ 1 + Drug * Putamen_ki.c + Session + (1 | ID)
Data: df_MPH
AIC BIC logLik deviance df.resid
1431.6 1453.9 -708.8 1417.6 173
Scaled residuals:
Min 1Q Median 3Q Max
-2.7177 -0.4466 0.0032 0.3182 3.5506
Random effects:
Groups Name Variance Std.Dev.
ID (Intercept) 175.69 13.255
Residual 57.99 7.615
Number of obs: 180, groups: ID, 90
Fixed effects:
Estimate Std. Error t value
(Intercept) 30.4948 2.1366 14.273
Drug1 1.3434 0.5704 2.355
Putamen_ki.c -0.1509 1.5168 -0.099
Session 1.5532 0.7851 1.978
Drug1:Putamen_ki.c 0.3515 0.5808 0.605
MPH_VS_Total <- lmer(Total ~ 1 + Drug*VS_ki + Session + (1 | ID), data = df_MPH,REML=F)
print(summary(MPH_VS_Total), corr=F) # print summary without fixed effect correlation matrix
Linear mixed model fit by maximum likelihood ['lmerMod']
Formula: Total ~ 1 + Drug * VS_ki + Session + (1 | ID)
Data: df_MPH
AIC BIC logLik deviance df.resid
1427.1 1449.4 -706.5 1413.1 173
Scaled residuals:
Min 1Q Median 3Q Max
-2.7614 -0.4458 -0.0326 0.3456 3.6077
Random effects:
Groups Name Variance Std.Dev.
ID (Intercept) 173.78 13.183
Residual 55.97 7.482
Number of obs: 180, groups: ID, 90
Fixed effects:
Estimate Std. Error t value
(Intercept) 16.5193 11.5621 1.429
Drug1 -6.7121 4.3339 -1.549
VS_ki 933.6037 778.5386 1.199
Session 1.7573 0.7712 2.279
Drug1:VS_ki 552.7085 294.7144 1.875
## Convergent PASTA corrected for Total number of ideas
MPH_Caudate_ConTotal <- lmer(Con_Total ~ 1 + Drug*Caudate_ki.c + Session + (1 | ID), data = df_MPH,REML=F)
print(summary(MPH_Caudate_ConTotal), corr=F) # print summary without fixed effect correlation matrix
Linear mixed model fit by maximum likelihood ['lmerMod']
Formula: Con_Total ~ 1 + Drug * Caudate_ki.c + Session + (1 | ID)
Data: df_MPH
AIC BIC logLik deviance df.resid
-49.1 -26.8 31.6 -63.1 173
Scaled residuals:
Min 1Q Median 3Q Max
-2.0418 -0.5060 0.1013 0.5229 2.1592
Random effects:
Groups Name Variance Std.Dev.
ID (Intercept) 0.03552 0.1885
Residual 0.01890 0.1375
Number of obs: 180, groups: ID, 90
Fixed effects:
Estimate Std. Error t value
(Intercept) 0.596737 0.034807 17.144
Drug1 -0.011848 0.010295 -1.151
Caudate_ki.c 0.013756 0.022486 0.612
Session 0.033278 0.013840 2.405
Drug1:Caudate_ki.c 0.006939 0.010337 0.671
MPH_Putamen_ConTotal <- lmer(Con_Total ~ 1 + Drug*Putamen_ki.c + Session + (1 | ID), data = df_MPH,REML=F)
print(summary(MPH_Putamen_ConTotal), corr=F) # print summary without fixed effect correlation matrix
Linear mixed model fit by maximum likelihood ['lmerMod']
Formula: Con_Total ~ 1 + Drug * Putamen_ki.c + Session + (1 | ID)
Data: df_MPH
AIC BIC logLik deviance df.resid
-48.6 -26.3 31.3 -62.6 173
Scaled residuals:
Min 1Q Median 3Q Max
-2.05005 -0.47652 0.08736 0.53785 2.16547
Random effects:
Groups Name Variance Std.Dev.
ID (Intercept) 0.03556 0.1886
Residual 0.01898 0.1378
Number of obs: 180, groups: ID, 90
Fixed effects:
Estimate Std. Error t value
(Intercept) 0.59940 0.03515 17.054
Drug1 -0.01175 0.01032 -1.139
Putamen_ki.c 0.01111 0.02250 0.494
Session 0.03190 0.01406 2.268
Drug1:Putamen_ki.c -0.00240 0.01050 -0.228
MPH_VS_ConTotal <- lmer(Con_Total ~ 1 + Drug*VS_ki + Session + (1 | ID), data = df_MPH,REML=F)
print(summary(MPH_VS_ConTotal), corr=F) # print summary without fixed effect correlation matrix
Linear mixed model fit by maximum likelihood ['lmerMod']
Formula: Con_Total ~ 1 + Drug * VS_ki + Session + (1 | ID)
Data: df_MPH
AIC BIC logLik deviance df.resid
-49.7 -27.3 31.8 -63.7 173
Scaled residuals:
Min 1Q Median 3Q Max
-2.06560 -0.50547 0.09341 0.53784 2.18044
Random effects:
Groups Name Variance Std.Dev.
ID (Intercept) 0.03536 0.1880
Residual 0.01886 0.1373
Number of obs: 180, groups: ID, 90
Fixed effects:
Estimate Std. Error t value
(Intercept) 0.44927 0.17328 2.593
Drug1 -0.07556 0.07952 -0.950
VS_ki 9.94044 11.60113 0.857
Session 0.03476 0.01401 2.481
Drug1:VS_ki 4.37213 5.40786 0.808
## Divergent PASTA corrected for Total number of ideas
MPH_Caudate_DivTotal <- lmer(Div_Total ~ 1 + Drug*Caudate_ki.c + Session + (1 | ID), data = df_MPH,REML=F)
print(summary(MPH_Caudate_DivTotal), corr=F) # print summary without fixed effect correlation matrix
Linear mixed model fit by maximum likelihood ['lmerMod']
Formula: Div_Total ~ 1 + Drug * Caudate_ki.c + Session + (1 | ID)
Data: df_MPH
AIC BIC logLik deviance df.resid
-49.0 -26.6 31.5 -63.0 173
Scaled residuals:
Min 1Q Median 3Q Max
-2.1579 -0.5219 -0.1046 0.5056 2.0404
Random effects:
Groups Name Variance Std.Dev.
ID (Intercept) 0.03551 0.1884
Residual 0.01893 0.1376
Number of obs: 180, groups: ID, 90
Fixed effects:
Estimate Std. Error t value
(Intercept) 0.40375 0.03482 11.594
Drug1 0.01191 0.01030 1.156
Caudate_ki.c -0.01377 0.02249 -0.612
Session -0.03339 0.01385 -2.410
Drug1:Caudate_ki.c -0.00700 0.01035 -0.677
MPH_Putamen_DivTotal <- lmer(Div_Total ~ 1 + Drug*Putamen_ki.c + Session + (1 | ID), data = df_MPH,REML=F)
print(summary(MPH_Putamen_DivTotal), corr=F) # print summary without fixed effect correlation matrix
Linear mixed model fit by maximum likelihood ['lmerMod']
Formula: Div_Total ~ 1 + Drug * Putamen_ki.c + Session + (1 | ID)
Data: df_MPH
AIC BIC logLik deviance df.resid
-48.4 -26.1 31.2 -62.4 173
Scaled residuals:
Min 1Q Median 3Q Max
-2.1643 -0.5388 -0.0868 0.4772 2.0479
Random effects:
Groups Name Variance Std.Dev.
ID (Intercept) 0.03554 0.1885
Residual 0.01901 0.1379
Number of obs: 180, groups: ID, 90
Fixed effects:
Estimate Std. Error t value
(Intercept) 0.401089 0.035166 11.406
Drug1 0.011812 0.010327 1.144
Putamen_ki.c -0.011061 0.022503 -0.492
Session -0.032006 0.014074 -2.274
Drug1:Putamen_ki.c 0.002371 0.010513 0.226
MPH_VS_DivTotal <- lmer(Div_Total ~ 1 + Drug*VS_ki + Session + (1 | ID), data = df_MPH,REML=F)
print(summary(MPH_VS_DivTotal), corr=F) # print summary without fixed effect correlation matrix
Linear mixed model fit by maximum likelihood ['lmerMod']
Formula: Div_Total ~ 1 + Drug * VS_ki + Session + (1 | ID)
Data: df_MPH
AIC BIC logLik deviance df.resid
-49.5 -27.2 31.8 -63.5 173
Scaled residuals:
Min 1Q Median 3Q Max
-2.17925 -0.53855 -0.09489 0.50574 2.06365
Random effects:
Groups Name Variance Std.Dev.
ID (Intercept) 0.03534 0.1880
Residual 0.01889 0.1375
Number of obs: 180, groups: ID, 90
Fixed effects:
Estimate Std. Error t value
(Intercept) 0.55136 0.17328 3.182
Drug1 0.07569 0.07960 0.951
VS_ki -9.95034 11.60081 -0.858
Session -0.03487 0.01402 -2.486
Drug1:VS_ki -4.37706 5.41288 -0.809
## Different ending PASTA corrected for Total number of ideas
MPH_Caudate_DifEndTotal <- lmer(DifEnd_Total ~ 1 + Drug*Caudate_ki.c + Session + (1 | ID), data = df_MPH,REML=F)
print(summary(MPH_Caudate_DifEndTotal), corr=F) # print summary without fixed effect correlation matrix
Linear mixed model fit by maximum likelihood ['lmerMod']
Formula: DifEnd_Total ~ 1 + Drug * Caudate_ki.c + Session + (1 | ID)
Data: df_MPH
AIC BIC logLik deviance df.resid
-206.3 -183.9 110.1 -220.3 173
Scaled residuals:
Min 1Q Median 3Q Max
-1.6374 -0.5502 -0.0632 0.4053 2.5076
Random effects:
Groups Name Variance Std.Dev.
ID (Intercept) 0.01609 0.12683
Residual 0.00748 0.08648
Number of obs: 180, groups: ID, 90
Fixed effects:
Estimate Std. Error t value
(Intercept) 0.318047 0.022444 14.171
Drug1 -0.008224 0.006477 -1.270
Caudate_ki.c 0.003667 0.014929 0.246
Session -0.018966 0.008733 -2.172
Drug1:Caudate_ki.c 0.001022 0.006503 0.157
MPH_Putamen_DifEndTotal <- lmer(DifEnd_Total ~ 1 + Drug*Putamen_ki.c + Session + (1 | ID), data = df_MPH,REML=F)
print(summary(MPH_Putamen_DifEndTotal), corr=F) # print summary without fixed effect correlation matrix
Linear mixed model fit by maximum likelihood ['lmerMod']
Formula: DifEnd_Total ~ 1 + Drug * Putamen_ki.c + Session + (1 | ID)
Data: df_MPH
AIC BIC logLik deviance df.resid
-209.1 -186.7 111.5 -223.1 173
Scaled residuals:
Min 1Q Median 3Q Max
-1.68184 -0.56604 -0.04624 0.34020 2.48106
Random effects:
Groups Name Variance Std.Dev.
ID (Intercept) 0.016166 0.1271
Residual 0.007259 0.0852
Number of obs: 180, groups: ID, 90
Fixed effects:
Estimate Std. Error t value
(Intercept) 0.313340 0.022434 13.967
Drug1 -0.008400 0.006382 -1.316
Putamen_ki.c -0.005445 0.014917 -0.365
Session -0.016524 0.008731 -1.893
Drug1:Putamen_ki.c 0.010897 0.006497 1.677
MPH_VS_DifEndTotal <- lmer(DifEnd_Total ~ 1 + Drug*VS_ki + Session + (1 | ID), data = df_MPH,REML=F)
print(summary(MPH_VS_DifEndTotal), corr=F) # print summary without fixed effect correlation matrix
Linear mixed model fit by maximum likelihood ['lmerMod']
Formula: DifEnd_Total ~ 1 + Drug * VS_ki + Session + (1 | ID)
Data: df_MPH
AIC BIC logLik deviance df.resid
-207.7 -185.4 110.9 -221.7 173
Scaled residuals:
Min 1Q Median 3Q Max
-1.61885 -0.52688 -0.04225 0.40725 2.41080
Random effects:
Groups Name Variance Std.Dev.
ID (Intercept) 0.015845 0.12588
Residual 0.007456 0.08635
Number of obs: 180, groups: ID, 90
Fixed effects:
Estimate Std. Error t value
(Intercept) 0.439868 0.114367 3.846
Drug1 -0.035845 0.050007 -0.717
VS_ki -8.430435 7.669035 -1.099
Session -0.018539 0.008835 -2.098
Drug1:VS_ki 1.896520 3.400667 0.558
bic_bf10(SUL2_bic,SUL1_bic) # null comes first, the results are for the null
[1] 0.1925223
bic_bf10(SUL3_bic,SUL1_bic) # convert BICs to BF
[1] 0.01479896
bic_bf10(SUL3_bic,SUL2_bic) # convert BICs to BF
[1] 0.07686883
---
title: "R Notebook"
output: html_notebook
---



```{r}
rm(list=ls()) # removes all variables from the workspace :)

# data scaled using only session 1 data below
data1 <- read.csv('~/Dropbox/DCCN/Creativity/CreativitySession1_Updated.csv')
data1$Putamen_ki.c <- scale(data1$Putamen_ki) #dataAll$Putamen_ki - mean(dataAll$Putamen_ki)
data1$Caudate_ki.c <- scale(data1$Caudate_ki) #dataAll$Caudate_ki - mean(dataAll$Caudate_ki)
data1$VS_ki.c <- scale(data1$VS_ki) #dataAll$VS_ki - mean(dataAll$VS_ki)
write.csv(data1,"~/Dropbox/DCCN/Creativity/CreativitySession1_2.csv", row.names = FALSE)
QInfo <- quantile(data1$Putamen_ki.c, prob=c(.20,.5,.80))

dataNew <- data1 %>% mutate(PutamenQuartile =
                     case_when(Putamen_ki.c <= QInfo[1] ~ "1", 
                               Putamen_ki.c >= QInfo[3] ~ "2")
)

write.csv(dataNew,"~/Dropbox/DCCN/Creativity/Creativity_session1_Quartiled.csv", row.names = FALSE)


```


```{r}
# and then added to the following workbook
dataAll <- read.csv('~/Dropbox/DCCN/Creativity/CreativityIDsheet_MissingDataRemoved_Updated.csv')
table(dataAll$Session, dataAll$Drug)
# turn drug conditions into factor levels
dataAll$Drug <- factor(dataAll$Drug, levels = c("MPH","SUL","PBO"));
dataAll$PutamenSplit <- factor(dataAll$PutamenSplit, levels = c("0","1"));
dataAll$CaudateSplit <- factor(dataAll$CaudateSplit, levels = c("0","1"));
dataAll$VSSplit <- factor(dataAll$VSSplit, levels = c("0","1"));


dataAll$DifEnd_Total <- dataAll$DifferentEnd/dataAll$Total # divide different end scores by total number of ideas
dataAll$DifEnd_Con <- dataAll$DifferentEnd/dataAll$Convergent_Pasta # divide different end scores by total number of ideas


# set contrasts to sum-to-zero
options(contrasts=c("contr.sum", "contr.poly"))

# set two dataframes for the contrast between MPH and PBO - between SUL and PBO
df_MPH <- dplyr::filter(dataAll, Drug %in% c("MPH","PBO"))  
df_SUL <- dplyr::filter(dataAll, Drug %in% c("SUL","PBO"))
data_PBO <- dplyr::filter(dataAll, Drug %in% c("PBO"))


```
```{r}

## PLACEBO
# does creativity vary also as a function of individual differences in dopamine synthesis capacity under baseline (under placebo)?

Placebo_RAT_Caudate <- lm(Convergent_RAT ~ 1 + Caudate_ki.c , data = data_PBO, REML=F)
Placebo_RAT_VS <- lm(Convergent_RAT ~ 1 + VS_ki.c , data = data_PBO, REML=F)
Placebo_RAT_Putamen <- lm(Convergent_RAT ~ 1 + Putamen_ki.c, data = data_PBO, REML=F)

Placebo_AUT_Caudate <- lm(AUT_divergent ~ 1 + Caudate_ki.c , data = data_PBO, REML=F)
Placebo_AUT_VS <- lm(AUT_divergent ~ 1 + VS_ki.c , data = data_PBO, REML=F)
Placebo_AUT_Putamen <- lm(AUT_divergent ~ 1 + Putamen_ki.c , data = data_PBO, REML=F)

Placebo_PASTA_CT_Caudate <- lm(Convergent_Pasta ~ 1 + Caudate_ki.c , data = data_PBO, REML=F)
Placebo_PASTA_CT_VS <- lm(Convergent_Pasta ~ 1 + VS_ki.c , data = data_PBO, REML=F)
Placebo_PASTA_CT_Putamen <- lm(Convergent_Pasta ~ 1 + Putamen_ki.c , data = data_PBO, REML=F)

Placebo_PASTA_DT_Caudate <- lm(Divergent_Pasta ~ 1 + Caudate_ki.c , data = data_PBO, REML=F)
Placebo_PASTA_DT_VS <- lm(Divergent_Pasta ~ 1 + VS_ki.c , data = data_PBO, REML=F)
Placebo_PASTA_DT_Putamen <- lm(Divergent_Pasta ~ 1 + Putamen_ki.c , data = data_PBO, REML=F)

Placebo_PASTA_DE_Caudate <- lm(DifferentEnd ~ 1 + Caudate_ki.c , data = data_PBO, REML=F)
Placebo_PASTA_DE_VS <- lm(DifferentEnd ~ 1 + VS_ki.c , data = data_PBO, REML=F)
Placebo_PASTA_DE_Putamen <- lm(DifferentEnd ~ 1 + Putamen_ki.c , data = data_PBO, REML=F)


print(summary(Placebo_RAT_Caudate), corr=F) # print summary without fixed effect correlation matrix
print(summary(Placebo_RAT_VS), corr=F) # print summary without fixed effect correlation matrix
print(summary(Placebo_RAT_Putamen), corr=F) # print summary without fixed effect correlation matrix

print(summary(Placebo_AUT_Caudate), corr=F) # print summary without fixed effect correlation matrix
print(summary(Placebo_AUT_VS), corr=F) # print summary without fixed effect correlation matrix
print(summary(Placebo_AUT_Putamen), corr=F) # print summary without fixed effect correlation matrix

print(summary(Placebo_PASTA_CT_Caudate), corr=F) # print summary without fixed effect correlation matrix
print(summary(Placebo_PASTA_CT_VS), corr=F) # print summary without fixed effect correlation matrix
print(summary(Placebo_PASTA_CT_Putamen), corr=F) # print summary without fixed effect correlation matrix

print(summary(Placebo_PASTA_DT_Caudate), corr=F) # print summary without fixed effect correlation matrix
print(summary(Placebo_PASTA_DT_VS), corr=F) # print summary without fixed effect correlation matrix
print(summary(Placebo_PASTA_DT_Putamen), corr=F) # print summary without fixed effect correlation matrix

print(summary(Placebo_PASTA_DE_Caudate), corr=F) # print summary without fixed effect correlation matrix
print(summary(Placebo_PASTA_DE_VS), corr=F) # print summary without fixed effect correlation matrix
print(summary(Placebo_PASTA_DE_Putamen), corr=F) # print summary without fixed effect correlation matrix



```

```{r}
##################### SUL #####################################################
##################### SUL #####################################################
##################### SUL #####################################################
##################### SUL #####################################################
##################### SUL #####################################################
```


```{r}
##################### RAT #####################################################

# Sulpiride*Caudate interaction in predicting Convergent RAT
SUL_Caudate_RAT <- lmer(Convergent_RAT ~ 1 + Drug*Caudate_ki.c + Session + (1 | ID), data = df_SUL, REML=F)
print(summary(SUL_Caudate_RAT), corr=F) # print summary without fixed effect correlation matrix
plot_model(SUL_Caudate_RAT, type = "pred", terms = c("Caudate_ki.c","Session","Drug"))
plot(SUL_Caudate_RAT)
qqnorm(residuals(SUL_Caudate_RAT))


# Sulpiride*Putamen interaction in predicting Convergent RAT
SUL_Putamen_RAT <- lmer(Convergent_RAT ~ 1 + Drug*Putamen_ki.c + Session + (1 | ID), data = df_SUL, REML=F)
print(summary(SUL_Putamen_RAT), corr=F) # print summary without fixed effect correlation matrix
plot_model(SUL_Putamen_RAT, type = "pred", terms = c("Putamen_ki.c","Session","Drug"))
plot(SUL_Putamen_RAT)
qqnorm(residuals(SUL_Putamen_RAT))


# Sulpiride*VS interaction in predicting Convergent RAT
SUL_VS_RAT <- lmer(Convergent_RAT ~ 1 + Drug*VS_ki.c + Session + (1 | ID), data = df_SUL, REML=F)
print(summary(SUL_VS_RAT), corr=F) # print summary without fixed effect correlation matrix
plot_model(SUL_VS_RAT, type = "pred", terms = c("VS_ki.c","Session","Drug"))
plot(SUL_VS_RAT)
qqnorm(residuals(SUL_VS_RAT))


Con1<- sjPlot::plot_model(SUL_Caudate_RAT, 
                   show.values=TRUE, show.p=TRUE, vline.color='black',
                   title="Caudate Ki") 

Con2 <- sjPlot::plot_model(SUL_Putamen_RAT, 
                   show.values=TRUE, show.p=TRUE, vline.color='black',
                   title="Putamen Ki") 

Con3 <- sjPlot::plot_model(SUL_VS_RAT, 
                   show.values=TRUE, show.p=TRUE, vline.color='black',
                   title="VS Ki") 

levels(Con1$data$term) <- c("SUL*Ki", "Session", "Ki", "SUL")
levels(Con2$data$term) <- c("SUL*Ki", "Session", "Ki", "SUL")
levels(Con3$data$term) <- c("SUL*Ki", "Session", "Ki", "SUL")

behavplotCon <- ggarrange(Con1, Con2, Con3,  ncol = 3,
  nrow = 1,   widths = 10,
  heights = 3)

annotate_figure(behavplotCon, top = text_grob("Effect of SUL on RAT convergent thinking", 
               color = "black", face = "bold", size = 21))

```

```{r}

##################### AUT #####################################################


# Sulpiride*Caudate interaction in predicting divergent AUT
SUL_Caudate_AUT <- lmer(AUT_divergent ~ 1 + Drug*Caudate_ki.c + Session + (1 | ID), data = df_SUL, REML=F)
print(summary(SUL_Caudate_AUT), corr=F) # print summary without fixed effect correlation matrix
plot_model(SUL_Caudate_AUT, type = "pred", terms = c("Caudate_ki.c","Session","Drug"))
plot(SUL_Caudate_AUT)
qqnorm(residuals(SUL_Caudate_AUT))

# Sulpiride*Caudate interaction in predicting divergent AUT
SUL_Putamen_AUT <- lmer(AUT_divergent ~ 1 + Drug*Putamen_ki.c + Session + (1 | ID), data = df_SUL, REML=F)
print(summary(SUL_Putamen_AUT), corr=F) # print summary without fixed effect correlation matrix
plot_model(SUL_Putamen_AUT, type = "pred", terms = c("Putamen_ki.c","Session","Drug"))
plot(SUL_Putamen_AUT)
qqnorm(residuals(SUL_Putamen_AUT))

# Sulpiride*VS interaction in predicting divergent AUT
SUL_VS_AUT <- lmer(AUT_divergent ~ 1 + Drug*VS_ki.c + Session + (1 | ID), data = df_SUL, REML=F)
print(summary(SUL_VS_AUT), corr=F) # print summary without fixed effect correlation matrix
plot_model(SUL_VS_AUT, type = "pred", terms = c("VS_ki.c","Session","Drug"))
plot(SUL_VS_AUT)
qqnorm(residuals(SUL_VS_AUT))


Div1<- sjPlot::plot_model(SUL_Caudate_AUT, 
                   show.values=TRUE, show.p=TRUE, vline.color='black',
                   title="Caudate Ki") 

Div2 <- sjPlot::plot_model(SUL_Putamen_AUT, 
                   show.values=TRUE, show.p=TRUE, vline.color='black',
                   title="Putamen Ki") 

Div3 <- sjPlot::plot_model(SUL_VS_AUT, 
                   show.values=TRUE, show.p=TRUE, vline.color='black',
                   title="VS Ki") 

levels(Div1$data$term) <- c("SUL*Ki", "Session", "Ki", "SUL")
levels(Div2$data$term) <- c("SUL*Ki", "Session", "Ki", "SUL")
levels(Div3$data$term) <- c("SUL*Ki", "Session", "Ki", "SUL")

behavplotDiv <- ggarrange(Div1, Div2, Div3,  ncol = 3,
  nrow = 1,   widths = 10,
  heights = 3)

annotate_figure(behavplotDiv, top = text_grob("Effect of SUL on AUT divergent thinking", 
               color = "black", face = "bold", size = 21))
```



```{r}
##################### PASTA #####################################################
##################### Convergent ###############################################


# Sulpiride*Caudate interaction in predicting convergent Pasta
SUL_Caudate_PastCon <- lmer(Convergent_Pasta ~ 1 + Drug*Caudate_ki.c + Session + (1 | ID), data = df_SUL, REML=F)
print(summary(SUL_Caudate_PastCon), corr=F) # print summary without fixed effect correlation matrix
plot_model(SUL_Caudate_PastCon, type = "pred", terms = c("Caudate_ki.c","Session","Drug"))
plot(SUL_Caudate_PastCon)
qqnorm(residuals(SUL_Caudate_PastCon))

# Sulpiride*Caudate interaction in predicting convergent Pasta
SUL_Putamen_PastCon <- lmer(Convergent_Pasta ~ 1 + Drug*Putamen_ki.c + Session + (1 | ID), data = df_SUL, REML=F)
print(summary(SUL_Putamen_PastCon), corr=F) # print summary without fixed effect correlation matrix
plot_model(SUL_Putamen_PastCon, type = "pred", terms = c("Putamen_ki.c","Session","Drug"))
plot(SUL_Putamen_PastCon)
qqnorm(residuals(SUL_Putamen_PastCon))

# Sulpiride*Caudate interaction in predicting convergent Pasta
SUL_VS_PastCon <- lmer(Convergent_Pasta ~ 1 + Drug*VS_ki.c + Session + (1 | ID), data = df_SUL, REML=F)
print(summary(SUL_VS_PastCon), corr=F) # print summary without fixed effect correlation matrix
plot_model(SUL_VS_PastCon, type = "pred", terms = c("VS_ki.c","Session","Drug"))
plot(SUL_VS_PastCon)
qqnorm(residuals(SUL_VS_PastCon))



Pasta1<- sjPlot::plot_model(SUL_Caudate_PastCon, 
                   show.values=TRUE, show.p=TRUE, vline.color='black',
                   title="Caudate Ki") 

Pasta2 <- sjPlot::plot_model(SUL_Putamen_PastCon, 
                   show.values=TRUE, show.p=TRUE, vline.color='black',
                   title="Putamen Ki") 

Pasta3 <- sjPlot::plot_model(SUL_VS_PastCon, 
                   show.values=TRUE, show.p=TRUE, vline.color='black',
                   title="VS Ki") 

levels(Pasta1$data$term) <- c("SUL*Ki", "Session", "Ki", "SUL")
levels(Pasta2$data$term) <- c("SUL*Ki", "Session", "Ki", "SUL")
levels(Pasta3$data$term) <- c("SUL*Ki", "Session", "Ki", "SUL")

behavplotPasta1 <- ggarrange(Pasta1, Pasta2, Pasta3,  ncol = 3,
  nrow = 1,   widths = 10,
  heights = 3)

annotate_figure(behavplotPasta1, top = text_grob("Effect of SUL on ANT convergent thinking", 
               color = "black", face = "bold", size = 21))

```


```{r}

##################### PASTA #####################################################
##################### Divergent ###############################################


# Sulpiride*Caudate interaction in predicting divergent Pasta
SUL_Caudate_PastDiv <- lmer(Divergent_Pasta ~ 1 + Drug*Caudate_ki.c + Session + (1 | ID), data = df_SUL, REML=F)
print(summary(SUL_Caudate_PastDiv), corr=F) # print summary without fixed effect correlation matrix
plot_model(SUL_Caudate_PastDiv, type = "pred", terms = c("Caudate_ki.c","Session","Drug"))
plot(SUL_Caudate_PastDiv)
qqnorm(residuals(SUL_Caudate_PastDiv))


# Sulpiride*Caudate interaction in predicting divergent Pasta
SUL_Putamen_PastDiv <- lmer(Divergent_Pasta ~ 1 + Drug*Putamen_ki.c + Session + (1 | ID), data = df_SUL, REML=F)
print(summary(SUL_Putamen_PastDiv), corr=F) # print summary without fixed effect correlation matrix
plot_model(SUL_Putamen_PastDiv, type = "pred", terms = c("Putamen_ki.c","Session","Drug"))
plot(SUL_Putamen_PastDiv)
qqnorm(residuals(SUL_Putamen_PastDiv))

# Sulpiride*VS interaction in predicting divergent Pasta
SUL_VS_PastDiv <- lmer(Divergent_Pasta ~ 1 + Drug*VS_ki.c + Session + (1 | ID), data = df_SUL, REML=F)
print(summary(SUL_VS_PastDiv), corr=F) # print summary without fixed effect correlation matrix
plot_model(SUL_VS_PastDiv, type = "pred", terms = c("VS_ki.c","Session","Drug"))
plot(SUL_VS_PastDiv)
qqnorm(residuals(SUL_VS_PastDiv))



Pasta4<- sjPlot::plot_model(SUL_Caudate_PastDiv, 
                   show.values=TRUE, show.p=TRUE, vline.color='black',
                   title="Caudate Ki") 

Pasta5 <- sjPlot::plot_model(SUL_Putamen_PastDiv, 
                   show.values=TRUE, show.p=TRUE, vline.color='black',
                   title="Putamen Ki") 

Pasta6 <- sjPlot::plot_model(SUL_VS_PastDiv, 
                   show.values=TRUE, show.p=TRUE, vline.color='black',
                   title="VS Ki") 

levels(Pasta4$data$term) <- c("SUL*Ki", "Session", "Ki", "SUL")
levels(Pasta5$data$term) <- c("SUL*Ki", "Session", "Ki", "SUL")
levels(Pasta6$data$term) <- c("SUL*Ki", "Session", "Ki", "SUL")

behavplotPasta2 <- ggarrange(Pasta4, Pasta5, Pasta6,  ncol = 3,
  nrow = 1,   widths = 10,
  heights = 3)

annotate_figure(behavplotPasta2, top = text_grob("Effect of SUL on ANT divergent thinking", 
               color = "black", face = "bold", size = 21))

```

```{r}
####### Different End PASTA ###################################################

# Sulpiride*Caudate interaction in predicting divergent Pasta divergent response
SUL_Caudate_PastDif <- lmer(DifferentEnd ~ 1 + Drug*Caudate_ki.c + Session + (1 | ID), data = df_SUL, REML=F)
print(summary(SUL_Caudate_PastDif), corr=F) # print summary without fixed effect correlation matrix
plot_model(SUL_Caudate_PastDif, type = "pred", terms = c("Caudate_ki.c","Session","Drug"))
plot(SUL_Caudate_PastDif)
qqnorm(residuals(SUL_Caudate_PastDif))

# Sulpiride*Putamen interaction in predicting divergent Pasta divergent response
SUL_Putamen_PastDif <- lmer(DifferentEnd ~ 1 + Drug*Putamen_ki.c + Session + (1 | ID), data = df_SUL, REML=F)
print(summary(SUL_Putamen_PastDif), corr=F) # print summary without fixed effect correlation matrix
plot_model(SUL_Putamen_PastDif, type = "pred", terms = c("Putamen_ki.c","Session","Drug"))
plot(SUL_Putamen_PastDif)
qqnorm(residuals(SUL_Putamen_PastDif))

# Sulpiride*VS interaction in predicting divergent Pasta divergent response
SUL_VS_PastDif <- lmer(DifferentEnd ~ 1 + Drug*VS_ki.c + Session + (1 | ID), data = df_SUL, REML=F)
print(summary(SUL_VS_PastDif), corr=F) # print summary without fixed effect correlation matrix
plot_model(SUL_VS_PastDif, type = "pred", terms = c("VS_ki.c","Session","Drug"))
plot(SUL_VS_PastDif)
qqnorm(residuals(SUL_VS_PastDif))




Pasta7<- sjPlot::plot_model(SUL_Caudate_PastDif, 
                   show.values=TRUE, show.p=TRUE, vline.color='black',
                   title="Caudate Ki") 

Pasta8 <- sjPlot::plot_model(SUL_Putamen_PastDif, 
                   show.values=TRUE, show.p=TRUE, vline.color='black',
                   title="Putamen Ki") 

Pasta9 <- sjPlot::plot_model(SUL_VS_PastDif, 
                   show.values=TRUE, show.p=TRUE, vline.color='black',
                   title="VS Ki") 

levels(Pasta7$data$term) <- c("SUL*Ki", "Session", "Ki", "SUL")
levels(Pasta8$data$term) <- c("SUL*Ki", "Session", "Ki", "SUL")
levels(Pasta9$data$term) <- c("SUL*Ki", "Session", "Ki", "SUL")

behavplotPasta3 <- ggarrange(Pasta7, Pasta8, Pasta9,  ncol = 3,
  nrow = 1,   widths = 10,
  heights = 3)

annotate_figure(behavplotPasta3, top = text_grob("Effect of SUL on ANT response divergence", 
               color = "black", face = "bold", size = 21))

```




```{r}
##################### MPH #####################################################
##################### MPH #####################################################
##################### MPH #####################################################
##################### MPH #####################################################
##################### MPH #####################################################
```


```{r}
##################### RAT #####################################################

# MPH*Caudate interaction in predicting Convergent RAT
MPH_Caudate_RAT <- lmer(Convergent_RAT ~ 1 + Drug*Caudate_ki.c + Session + (1 | ID), data = df_MPH, REML=F)
print(summary(MPH_Caudate_RAT), corr=F) # print summary without fixed effect correlation matrix
plot_model(MPH_Caudate_RAT, type = "pred", terms = c("Caudate_ki.c","Session","Drug"))
plot(MPH_Caudate_RAT)
qqnorm(residuals(MPH_Caudate_RAT))

# MPH*Putamen interaction in predicting Convergent RAT
MPH_Putamen_RAT <- lmer(Convergent_RAT ~ 1 + Drug*Putamen_ki.c + Session + (1 | ID), data = df_MPH, REML=F)
print(summary(MPH_Putamen_RAT), corr=F) # print summary without fixed effect correlation matrix
plot_model(MPH_Putamen_RAT, type = "pred", terms = c("Putamen_ki.c","Session","Drug"))
plot(MPH_Putamen_RAT)
qqnorm(residuals(MPH_Putamen_RAT))

# Sulpiride*VS interaction in predicting Convergent RAT
MPH_VS_RAT <- lmer(Convergent_RAT ~ 1 + Drug*VS_ki.c + Session + (1 | ID), data = df_MPH, REML=F)
print(summary(MPH_VS_RAT), corr=F) # print summary without fixed effect correlation matrix
plot_model(MPH_VS_RAT, type = "pred", terms = c("VS_ki.c","Session","Drug"))
plot(MPH_VS_RAT)
qqnorm(residuals(MPH_VS_RAT))
```

```{r}
##################### AUT #####################################################

# MPH*Caudate interaction in predicting divergent AUT
MPH_Caudate_AUT <- lmer(AUT_divergent ~ 1 + Drug*Caudate_ki.c + Session + (1 | ID), data = df_MPH, REML=F)
print(summary(MPH_Caudate_AUT), corr=F) # print summary without fixed effect correlation matrix
plot_model(MPH_Caudate_AUT, type = "pred", terms = c("Caudate_ki.c","Session","Drug"))
plot(MPH_Caudate_AUT)
qqnorm(residuals(MPH_Caudate_AUT))

# MPH*Putamen interaction in predicting divergent AUT
MPH_Putamen_AUT <- lmer(AUT_divergent ~ 1 + Drug*Putamen_ki.c + Session + (1 | ID), data = df_MPH, REML=F)
print(summary(MPH_Putamen_AUT), corr=F) # print summary without fixed effect correlation matrix
plot_model(MPH_Putamen_AUT, type = "pred", terms = c("Putamen_ki.c","Session","Drug"))
plot(MPH_Putamen_AUT)
qqnorm(residuals(MPH_Putamen_AUT))

# MPH*VS interaction in predicting divergent AUT
MPH_VS_AUT <- lmer(AUT_divergent ~ 1 + Drug*VS_ki.c + Session + (1 | ID), data = df_MPH, REML=F)
print(summary(MPH_VS_AUT), corr=F) # print summary without fixed effect correlation matrix
plot_model(MPH_VS_AUT, type = "pred", terms = c("VS_ki.c","Session","Drug"))
plot(MPH_VS_AUT)
qqnorm(residuals(MPH_VS_AUT))
```



```{r}

##################### PASTA #####################################################
##################### Convergent ##############################################

# MPH*Caudate interaction in predicting convergent Pasta
MPH_Caudate_PastCon <- lmer(Convergent_Pasta ~ 1 + Drug*Caudate_ki.c + Session + (1 | ID), data = df_MPH,REML=F)
print(summary(MPH_Caudate_PastCon), corr=F) # print summary without fixed effect correlation matrix
plot_model(MPH_Caudate_PastCon, type = "pred", terms = c("Caudate_ki.c","Session","Drug"))
plot(MPH_Caudate_PastCon)
qqnorm(residuals(MPH_Caudate_PastCon))


# MPH*Putamen interaction in predicting convergent Pasta
MPH_Putamen_PastCon <- lmer(Convergent_Pasta ~ 1 + Drug*Putamen_ki.c + Session + (1 | ID), data = df_MPH, REML=F)
print(summary(MPH_Putamen_PastCon), corr=F) # print summary without fixed effect correlation matrix
plot_model(MPH_Putamen_PastCon, type = "pred", terms = c("Putamen_ki.c","Session","Drug"))
plot(MPH_Putamen_PastCon)
qqnorm(residuals(MPH_Putamen_PastCon))

# MPH*VS interaction in predicting convergent Pasta
MPH_VS_PastCon <- lmer(Convergent_Pasta ~ 1 + Drug*VS_ki.c + Session + (1 | ID), data = df_MPH, REML=F)
print(summary(MPH_VS_PastCon), corr=F) # print summary without fixed effect correlation matrix
plot_model(MPH_VS_PastCon, type = "pred", terms = c("VS_ki.c","Session","Drug"))
plot(MPH_VS_PastCon)
qqnorm(residuals(MPH_VS_PastCon))


set_theme(
  base = theme_classic(), 
  legend.title.face = "italic", # title font face
  legend.inside = TRUE,         # legend inside plot
  legend.color = "grey50",      # legend label color
  legend.pos = "bottom right",  # legend position inside plot
  title.size = 2,
  title.align = "center",
  axis.title.size = 1.1,
  axis.textsize = 1.1,
  legend.size = 1,
  legend.title.size = 2,
  geom.label.size = 3
)

Convergent1 <- sjPlot::plot_model(MPH_Caudate_PastCon,
                   show.values=TRUE, show.p=TRUE, vline.color='black', 
                   title="Caudate Ki") 

Convergent2 <- sjPlot::plot_model(MPH_Putamen_PastCon, 
                   show.values=TRUE, show.p=TRUE,vline.color='black',
                   title="Putamen Ki") 

Convergent3 <- sjPlot::plot_model(MPH_VS_PastCon, 
                   show.values=TRUE, show.p=TRUE, vline.color='black',
                   title="VS Ki") 

levels(Convergent1$data$term) <- c("MPH*Ki", "Session", "Ki", "MPH")
levels(Convergent2$data$term) <- c("MPH*Ki", "Session", "Ki", "MPH")
levels(Convergent3$data$term) <- c("MPH*Ki", "Session", "Ki", "MPH")


behavplot <- ggarrange(Convergent1, Convergent2, Convergent3,  ncol = 3,
  nrow = 1,   widths = 10,
  heights = 3)

annotate_figure(behavplot, top = text_grob("Effect of MPH on convergent thinking", 
               color = "black", face = "bold", size = 21))

```

```{r}

##################### PASTA #####################################################
##################### Divergent ##############################################

# MPH*Caudate interaction in predicting divergent Pasta
MPH_Caudate_PastDiv <- lmer(Divergent_Pasta ~ 1 + Drug*Caudate_ki.c + Session + (1 | ID), data = df_MPH, REML=F)
print(summary(MPH_Caudate_PastDiv), corr=F) # print summary without fixed effect correlation matrix
plot_model(MPH_Caudate_PastDiv, type = "pred", terms = c("Caudate_ki.c","Session","Drug"))
plot(MPH_Caudate_PastDiv)
qqnorm(residuals(MPH_Caudate_PastDiv))

# MPH*Putamen interaction in predicting divergent Pasta
MPH_Putamen_PastDiv <- lmer(Divergent_Pasta ~ 1 + Drug*Putamen_ki.c + Session + (1 | ID), data = df_MPH, REML=F)
print(summary(MPH_Putamen_PastDiv), corr=F) # print summary without fixed effect correlation matrix
plot_model(MPH_Putamen_PastDiv, type = "pred", terms = c("Putamen_ki.c","Session","Drug"))
plot(MPH_Putamen_PastDiv)
qqnorm(residuals(MPH_Putamen_PastDiv))

# MPH*VS interaction in predicting divergent Pasta
MPH_VS_PastDiv <- lmer(Divergent_Pasta ~ 1 + Drug*VS_ki.c + Session + (1 | ID), data = df_MPH, REML=F)
print(summary(MPH_VS_PastDiv), corr=F) # print summary without fixed effect correlation matrix
plot_model(MPH_VS_PastDiv, type = "pred", terms = c("VS_ki.c","Session","Drug"))
plot(MPH_VS_PastDiv)
qqnorm(residuals(MPH_VS_PastDiv))

Divergent1<- sjPlot::plot_model(MPH_Caudate_PastDiv, 
                   show.values=TRUE, show.p=TRUE, vline.color='black',
                   title="Caudate Ki") 

Divergent2 <- sjPlot::plot_model(MPH_Putamen_PastDiv, 
                   show.values=TRUE, show.p=TRUE, vline.color='black',
                   title="Putamen Ki") 

Divergent3 <- sjPlot::plot_model(MPH_VS_PastDiv, 
                   show.values=TRUE, show.p=TRUE, vline.color='black',
                   title="VS Ki") 

levels(Divergent1$data$term) <- c("MPH*Ki", "Session", "Ki", "MPH")
levels(Divergent2$data$term) <- c("MPH*Ki", "Session", "Ki", "MPH")
levels(Divergent3$data$term) <- c("MPH*Ki", "Session", "Ki", "MPH")

behavplot2 <- ggarrange(Divergent1, Divergent2, Divergent3,  ncol = 3,
  nrow = 1,   widths = 10,
  heights = 3)

annotate_figure(behavplot2, top = text_grob("Effect of MPH on divergent thinking", 
               color = "black", face = "bold", size = 21))


```

```{r}
####### Different End PASTA ###################################################

# MPH*Caudate interaction in predicting divergent Pasta divergent response
MPH_Caudate_PastDif <- lmer(DifferentEnd ~ 1 + Drug*Caudate_ki.c + Session + (1 | ID), data = df_MPH, REML=F)
print(summary(MPH_Caudate_PastDif), corr=F) # print summary without fixed effect correlation matrix
plot_model(MPH_Caudate_PastDif, type = "pred", terms = c("Caudate_ki.c","Session","Drug"))
plot(MPH_Caudate_PastDif)
qqnorm(residuals(MPH_Caudate_PastDif))

# MPH*Putamen interaction in predicting divergent Pasta divergent response
MPH_Putamen_PastDif <- lmer(DifferentEnd ~ 1 + Drug*Putamen_ki.c + Session + (1 | ID), data = df_MPH, REML=F)
print(summary(MPH_Putamen_PastDif), corr=F) # print summary without fixed effect correlation matrix
plot_model(MPH_Putamen_PastDif, type = "pred", terms = c("Putamen_ki.c","Session","Drug"))
plot(MPH_Putamen_PastDif)
qqnorm(residuals(MPH_Putamen_PastDif))

# MPH*VS interaction in predicting divergent Pasta divergent response
MPH_VS_PastDif <- lmer(DifferentEnd ~ 1 + Drug*VS_ki.c + Session + (1 | ID), data = df_MPH, REML=F)
print(summary(MPH_VS_PastDif), corr=F) # print summary without fixed effect correlation matrix
plot_model(MPH_VS_PastDif, type = "pred", terms = c("VS_ki.c","Session","Drug"))
plot(MPH_VS_PastDif)
qqnorm(residuals(MPH_VS_PastDif))


AllDif <- lmer(DifferentEnd ~ 1 + Drug*VS_ki.c+ Drug*Putamen_ki.c + Drug*Caudate_ki.c + Session + (1 | ID), data = df_MPH, REML=F)
print(summary(AllDif), corr=F) # print summary without fixed effect correlation matrix


DifEnd1<- sjPlot::plot_model(MPH_Caudate_PastDif, 
                   show.values=TRUE, show.p=TRUE, vline.color='black',
                   title="Caudate Ki") 

DifEnd2 <- sjPlot::plot_model(MPH_Putamen_PastDif, 
                   show.values=TRUE, show.p=TRUE, vline.color='black',
                   title="Putamen Ki") 

DifEnd3 <- sjPlot::plot_model(MPH_VS_PastDif, 
                   show.values=TRUE, show.p=TRUE,  vline.color='black',
                   title="VS Ki") 

levels(DifEnd1$data$term) <- c("MPH*Ki", "Session", "Ki", "MPH")
levels(DifEnd2$data$term) <- c("MPH*Ki", "Session", "Ki", "MPH")
levels(DifEnd3$data$term) <- c("MPH*Ki", "Session", "Ki", "MPH")

behavplot3 <- ggarrange(DifEnd1, DifEnd2, DifEnd3,  ncol = 3,
  nrow = 1,   widths = 10,
  heights = 3)

annotate_figure(behavplot3, top = text_grob("Effect of MPH on response divergence", 
               color = "black", face = "bold", size = 21))


```

```{r}
Session1 <- dataAll[grep("1", dataAll$Session),]

ggscatter(Session1, x = "Caudate_ki", y = "KDOCStotal",
          add = "reg.line", conf.int = TRUE,
          cor.coef = TRUE, cor.method = "pearson",
          xlab = "CaudateKi)", ylab = "SubjectiveCreativity")

ggscatter(Session1, x = "Putamen_ki", y = "KDOCStotal",
          add = "reg.line", conf.int = TRUE,
          cor.coef = TRUE, cor.method = "pearson",
          xlab = "CaudateKi)", ylab = "SubjectiveCreativity")

ggscatter(Session1, x = "VS_ki", y = "KDOCStotal",
          add = "reg.line", conf.int = TRUE,
          cor.coef = TRUE, cor.method = "pearson",
          xlab = "CaudateKi)", ylab = "SubjectiveCreativity")


```

```{r}
# Ecological validity

ConCr <- lmer(Convergent_Pasta ~ 1 + KDOCStotal + Session + (1 | ID), data = dataAll, REML=F) 
print(summary(ConCr), corr=F) # print summary without fixed effect correlation matrix

DivArt <- lmer(Divergent_Pasta ~ 1 + KDOCStotal + Session + (1 | ID), data = dataAll, REML=F) 
print(summary(DivArt), corr=F) # print summary without fixed effect correlation matrix

DifEnd <- lmer(DifferentEnd ~ 1 + KDOCStotal + Session + (1 | ID), data = dataAll, REML=F) 
print(summary(DifEnd), corr=F) # print summary without fixed effect correlation matrix

ConCr <- lmer(Convergent_RAT ~ 1 + KDOCStotal + Session + (1 | ID), data = dataAll, REML=F) 
print(summary(ConCr), corr=F) # print summary without fixed effect correlation matrix

ConCr <- lmer(AUT_divergent ~ 1 + KDOCStotal + Session + (1 | ID), data = dataAll, REML=F) 
print(summary(ConCr), corr=F) # print summary without fixed effect correlation matrix
```

```{r}

## criterion validity

ConDivPasta <- lmer(Convergent_Pasta ~ 1 + Divergent_Pasta + Session + (1 | ID), data = dataAll, REML=F) 
print(summary(ConDivPasta), corr=F) # print summary without fixed effect correlation matrix

ConDifPasta <- lmer(Convergent_Pasta ~ 1 + DifferentEnd + Session + (1 | ID), data = dataAll, REML=F) 
print(summary(ConDifPasta), corr=F) # print summary without fixed effect correlation matrix

## construct validity


ConDifRAT <- lmer(Convergent_RAT ~ 1 + DifferentEnd + Session + (1 | ID), data = dataAll, REML=F) 
print(summary(ConDifRAT), corr=F) # print summary without fixed effect correlation matrix

ConPastaRAT <- lmer(Convergent_RAT ~ 1 + Convergent_Pasta + Session + (1 | ID), data = dataAll, REML=F) 
print(summary(ConPastaRAT), corr=F) # print summary without fixed effect correlation matrix

DivPastaAUT <- lmer(AUT_divergent ~ 1 + Divergent_Pasta + Session + (1 | ID), data = dataAll, REML=F) 
print(summary(DivPastaAUT), corr=F) # print summary without fixed effect correlation matrix

DifPastaAUT <- lmer(AUT_divergent ~ 1 + DifferentEnd + Session + (1 | ID), data = dataAll, REML=F) 
print(summary(DifPastaAUT), corr=F) # print summary without fixed effect correlation matrix

ConDivRATAUT <- lmer(Convergent_RAT ~ 1 + AUT_divergent + Session + (1 | ID), data = dataAll, REML=F) 
print(summary(ConDivRATAUT), corr=F) # print summary without fixed effect correlation matrix


```

```{r}
if (!require(remotes)) {
    install.packages("remotes")
}
remotes::install_github('jorvlan/raincloudplots')

library(raincloudplots)

library(forcats)

# https://github.com/jorvlan/raincloudplots
```
```{r}
plot_data<-df_MPH[c("DifferentEnd","PutamenSplit","ID","Drug")]
colnames(plot_data)[1]<-"y_axis"
colnames(plot_data)[3]<-"id"
colnames(plot_data)[4]<-"group"


plot_data2 <- plot_data %>% mutate(x_axis =
                     case_when((PutamenSplit == 0 & group == "PBO") ~ 1, 
                               (PutamenSplit == 1 & group  == "PBO") ~ 1,
                               (PutamenSplit == 0 & group  == "MPH") ~ 2,
                               (PutamenSplit == 1 & group  == "MPH") ~ 2))
temp<-plot_data$PutamenSplit

plot_data$PutamenSplit<-plot_data2$x_axis
colnames(plot_data)[2]<-"x_axis"

for (i in 1:180)
{
plot_data[i,5] <- plot_data[i,2] + runif(1, 0, 0.09)
}
colnames(plot_data)[5]<-"jit"

plot_data[,6] <-temp
colnames(plot_data)[6]<-"PutamenSplit"

plot_data <- plot_data %>% mutate(PutamenSplit =
                     case_when((PutamenSplit == 0) ~ "low Ki", 
                               (PutamenSplit == 1 ) ~ "high Ki"))
                          
# first column is the y-axis scores, second column is the placement of the box plots and should vary between 1,1,01,2,2.01 based on where the means should be displayed and called x_axis, third column is Ids and named id, fourth column is the group like drug group, 5th column is the jitter and should be random numbers between 1 and 2

set_theme(
  base = theme_classic((base_size = 18)), 
  legend.title.face = "italic", # title font face
  legend.inside = TRUE,         # legend inside plot
  legend.color = "grey50",      # legend label color
  legend.pos = "bottom right",  # legend position inside plot
  title.size = 4,
  title.align = "center",
  axis.title.size = 1.3,
  axis.textsize = 1.1,
  legend.size = 1,
  legend.title.size = 2,
  geom.label.size = 3, 
)


raincloud_2x2 <- raincloud_2x2_repmes(
  data = plot_data,
  colors = (c('dodgerblue', 'darkorange', 'dodgerblue', 'darkorange')),
  fills = (c('dodgerblue', 'darkorange', 'dodgerblue', 'darkorange')),
  line_color = 'gray',
  line_alpha = .3,
  size = 1,
  alpha = .6,
  spread_x_ticks = TRUE) +  facet_wrap(~fct_rev(PutamenSplit)) +

scale_x_continuous(breaks=c(1,2), labels=c("Placebo", "MPH"), limits=c(0, 3)) +
  xlab("Drug") + 
  ylab("Response Divergence") 

raincloud_2x2 

ggsave('dataRD.tiff', units="in", width=15, height=6, dpi=300, compression = 'lzw')


```

```{r}
dataSkippedRAT <- read.csv('~/Dropbox/DCCN/Creativity/SkippedItemsRAT_Updated.csv')

dataSkipped <- lmer(SkippedItems ~ 1 + Session + (1 | subject), data = dataSkippedRAT, REML=F)
print(summary(dataSkipped), corr=F) # print summary without fixed effect correlation matrix


```

```{r}
install.packages("PerformanceAnalytics")
library(PerformanceAnalytics)

myvars <- c("Convergent_Pasta", "Divergent_Pasta", "Con_Div", "DifferentEnd", "AUT_divergent", "Convergent_RAT","AUT_RAT_diff", "Div_Con_Pasta_Dif","IP_Dif1", "IP_Dif2", "IP_Dif3", "IP_Dif4", "Deviance_Dif1", "Deviance_Dif2", "Deviance_Dif3", "Deviance_Dif4","Redo_Dif1", "Redo_Dif2", "Redo_Dif3", "Redo_Dif4","IP_Av_Dif","Redo_Av_Dif","Deviance_Av_Dif")

onlyAvs <- c("Convergent_Pasta", "Divergent_Pasta", "Con_Div", "DifferentEnd", "AUT_divergent", "Convergent_RAT","AUT_RAT_diff", "Div_Con_Pasta_Dif","IP_Av_Dif","Redo_Av_Dif","Deviance_Av_Dif")


newdata <- data_PBO[myvars]
Avdata <- data_PBO[onlyAvs]


chart.Correlation(newDeviancedata, histogram = TRUE, method = "pearson")
chart.Correlation(newRedodata, histogram = TRUE, method = "pearson")
chart.Correlation(newIPdata, histogram = TRUE, method = "pearson")


library(corrplot)

M = cor(newdata,use="pairwise.complete.obs")
testRes = cor.mtest(newdata, conf.level = 0.95)
corrplot(M, method = 'number') # colorful number
## leave blank on non-significant coefficient
## add significant correlation coefficients
corrplot(M, p.mat = testRes$p, method = 'circle', type = 'lower', insig='blank',
         addCoef.col ='black', number.cex = 0.8, diag=FALSE)

M = cor(Avdata,use="pairwise.complete.obs")
testRes = cor.mtest(Avdata, conf.level = 0.95)
corrplot(M, method = 'number') # colorful number
## leave blank on non-significant coefficient
## add significant correlation coefficients
corrplot(M, p.mat = testRes$p, method = 'circle', type = 'lower', insig='blank',
         addCoef.col ='black', number.cex = 0.8, diag=FALSE)

data_MPH <- dplyr::filter(dataAll, Drug %in% c("MPH"))  
data_MPH <- data_MPH[order(data_MPH$ID),]
data_SUL <- dplyr::filter(dataAll, Drug %in% c("SUL"))
data_SUL <- data_SUL[order(data_SUL$ID),]
data_PBO <- dplyr::filter(dataAll, Drug %in% c("PBO"))
data_PBO <- data_PBO[order(data_PBO$ID),]


df_Correlations <- data.frame(matrix(ncol = 8, nrow = 93))
x <- c("ID", "MPH_PBO_Redo_Av", "MPH_PBO_IP_Av","MPH_PBO_AUT_divergent","MPH_PBO_Divergent_Pasta","MPH_PBO_DifferentEnd","MPH_PBO_Convergent_RAT","MPH_PBO_Convergent_PASTA")
colnames(df_Correlations) <- x

df_Correlations$ID <- data_MPH$ID
df_Correlations$MPH_PBO_Redo_Av <- data_MPH$Redo_Av_Dif-data_PBO$Redo_Av_Dif
df_Correlations$MPH_PBO_IP_Av <- data_MPH$IP_Av_Dif-data_PBO$IP_Av_Dif
df_Correlations$MPH_PBO_AUT_divergent <- data_MPH$AUT_divergent-data_PBO$AUT_divergent
df_Correlations$MPH_PBO_Divergent_Pasta <- data_MPH$Divergent_Pasta-data_PBO$Divergent_Pasta
df_Correlations$MPH_PBO_DifferentEnd <- data_MPH$DifferentEnd-data_PBO$DifferentEnd
df_Correlations$MPH_PBO_Convergent_RAT <- data_MPH$Convergent_RAT-data_PBO$Convergent_RAT
df_Correlations$MPH_PBO_Convergent_PASTA <- data_MPH$Convergent_Pasta-data_PBO$Convergent_Pasta



M = cor(df_Correlations,use="pairwise.complete.obs")
testRes = cor.mtest(df_Correlations, conf.level = 0.95)
corrplot(M, method = 'number') # colorful number
## leave blank on non-significant coefficient
## add significant correlation coefficients
corrplot(M, p.mat = testRes$p, method = 'circle', type = 'lower', insig='blank',
         addCoef.col ='black', number.cex = 0.8, diag=FALSE)



df_SUL_cor <- data.frame(matrix(ncol = 8, nrow = 93))
x <- c("ID", "SUL_PBO_Redo_Av", "SUL_PBO_IP_Av","SUL_PBO_AUT_divergent","SUL_PBO_Divergent_Pasta","SUL_PBO_DifferentEnd","SUL_PBO_Convergent_RAT","SUL_PBO_Convergent_PASTA")
colnames(df_SUL_cor) <- x


df_SUL_cor$ID <- data_SUL$ID
df_SUL_cor$SUL_PBO_Redo_Av <- data_SUL$Redo_Av_Dif-data_PBO$Redo_Av_Dif
df_SUL_cor$SUL_PBO_IP_Av <- data_SUL$IP_Av_Dif-data_PBO$IP_Av_Dif
df_SUL_cor$SUL_PBO_AUT_divergent <- data_SUL$AUT_divergent-data_PBO$AUT_divergent
df_SUL_cor$SUL_PBO_Divergent_Pasta <- data_SUL$Divergent_Pasta-data_PBO$Divergent_Pasta
df_SUL_cor$SUL_PBO_DifferentEnd <- data_SUL$DifferentEnd-data_PBO$DifferentEnd
df_SUL_cor$SUL_PBO_Convergent_RAT <- data_SUL$Convergent_RAT-data_PBO$Convergent_RAT
df_SUL_cor$SUL_PBO_Convergent_PASTA <- data_SUL$Convergent_Pasta-data_PBO$Convergent_Pasta

M = cor(df_SUL_cor,use="pairwise.complete.obs")
testRes = cor.mtest(df_SUL_cor, conf.level = 0.95)
corrplot(M, method = 'number') # colorful number
## leave blank on non-significant coefficient
## add significant correlation coefficients
corrplot(M, p.mat = testRes$p, method = 'circle', type = 'lower', insig='blank',
         addCoef.col ='black', number.cex = 0.8, diag=FALSE)


```

```{r}
# Total number of ideas in PASTA
MPH_Caudate_Total <- lmer(Total ~ 1 + Drug*Caudate_ki.c + Session + (1 | ID), data = df_MPH,REML=F)
print(summary(MPH_Caudate_Total), corr=F) # print summary without fixed effect correlation matrix

MPH_Putamen_Total <- lmer(Total ~ 1 + Drug*Putamen_ki.c + Session + (1 | ID), data = df_MPH,REML=F)
print(summary(MPH_Putamen_Total), corr=F) # print summary without fixed effect correlation matrix

MPH_VS_Total <- lmer(Total ~ 1 + Drug*VS_ki + Session + (1 | ID), data = df_MPH,REML=F)
print(summary(MPH_VS_Total), corr=F) # print summary without fixed effect correlation matrix

## Convergent PASTA corrected for Total number of ideas
MPH_Caudate_ConTotal <- lmer(Con_Total ~ 1 + Drug*Caudate_ki.c + Session + (1 | ID), data = df_MPH,REML=F)
print(summary(MPH_Caudate_ConTotal), corr=F) # print summary without fixed effect correlation matrix

MPH_Putamen_ConTotal <- lmer(Con_Total ~ 1 + Drug*Putamen_ki.c + Session + (1 | ID), data = df_MPH,REML=F)
print(summary(MPH_Putamen_ConTotal), corr=F) # print summary without fixed effect correlation matrix

MPH_VS_ConTotal <- lmer(Con_Total ~ 1 + Drug*VS_ki + Session + (1 | ID), data = df_MPH,REML=F)
print(summary(MPH_VS_ConTotal), corr=F) # print summary without fixed effect correlation matrix

## Divergent PASTA corrected for Total number of ideas
MPH_Caudate_DivTotal <- lmer(Div_Total ~ 1 + Drug*Caudate_ki.c + Session + (1 | ID), data = df_MPH,REML=F)
print(summary(MPH_Caudate_DivTotal), corr=F) # print summary without fixed effect correlation matrix

MPH_Putamen_DivTotal <- lmer(Div_Total ~ 1 + Drug*Putamen_ki.c + Session + (1 | ID), data = df_MPH,REML=F)
print(summary(MPH_Putamen_DivTotal), corr=F) # print summary without fixed effect correlation matrix

MPH_VS_DivTotal <- lmer(Div_Total ~ 1 + Drug*VS_ki + Session + (1 | ID), data = df_MPH,REML=F)
print(summary(MPH_VS_DivTotal), corr=F) # print summary without fixed effect correlation matrix

## Different ending PASTA corrected for Total number of ideas
MPH_Caudate_DifEndTotal <- lmer(DifEnd_Total ~ 1 + Drug*Caudate_ki.c + Session + (1 | ID), data = df_MPH,REML=F)
print(summary(MPH_Caudate_DifEndTotal), corr=F) # print summary without fixed effect correlation matrix

MPH_Putamen_DifEndTotal <- lmer(DifEnd_Total ~ 1 + Drug*Putamen_ki.c + Session + (1 | ID), data = df_MPH,REML=F)
print(summary(MPH_Putamen_DifEndTotal), corr=F) # print summary without fixed effect correlation matrix

MPH_VS_DifEndTotal <- lmer(DifEnd_Total ~ 1 + Drug*VS_ki + Session + (1 | ID), data = df_MPH,REML=F)
print(summary(MPH_VS_DifEndTotal), corr=F) # print summary without fixed effect correlation matrix

```

```{r}

df_MPH_LowQ <- dplyr::filter(df_MPH, PutamenQuartile %in% c("1"))  
df_MPH_HighQ <- dplyr::filter(df_MPH, PutamenQuartile %in% c("2"))  

MPH_Putamen_PastDif_LowQ <- lmer(DifferentEnd ~ 1 + Drug + Session + (1 | ID), data = df_MPH_LowQ, REML=F)
print(summary(MPH_Putamen_PastDif_LowQ), corr=F) # print summary without fixed effect correlation matrix

MPH_Putamen_PastDif_HiQ <- lmer(DifferentEnd ~ 1 + Drug + Session + (1 | ID), data = df_MPH_HighQ, REML=F)
print(summary(MPH_Putamen_PastDif_HiQ), corr=F) # print summary without fixed effect correlation matrix

library(BayesFactor)

lm1_bic <- BIC(MPH_Putamen_PastDif_LowQ) 
MPH_Putamen_PastDif_LowQ_NoDrug <- lmer(DifferentEnd ~ 1 + Session + (1 | ID), data = df_MPH_LowQ, REML=F)


lm2_bic <- BIC(MPH_Putamen_PastDif_LowQ_NoDrug) 

bic_bf10 <- function(null, alternative) {
new_bf <- exp((null - alternative) / 2) # convert BICs to Bayes factor
names(new_bf) <- NULL # remove BIC label
return(new_bf) # return Bayes factor of alternative over null hypothesis
}

bic_bf10(lm2_bic,lm1_bic) # convert BICs to BF


SUL1 <- lmer(DifferentEnd ~ 1 + Drug*Putamen_ki.c + Session + (1 | ID), data = df_SUL, REML=F)
print(summary(SUL1), corr=F) # print summary without fixed effect correlation matrix

SUL2 <- lmer(DifferentEnd ~ 1 + Drug + Putamen_ki.c + Session + (1 | ID), data = df_SUL, REML=F)
print(summary(SUL2), corr=F) # print summary without fixed effect correlation matrix

SUL3 <- lmer(DifferentEnd ~ 1 + Putamen_ki.c + Session + (1 | ID), data = df_SUL, REML=F)
print(summary(SUL3), corr=F) # print summary without fixed effect correlation matrix

SUL1_bic <- BIC(SUL1) 
SUL2_bic <- BIC(SUL2) 
SUL3_bic <- BIC(SUL3) 

bic_bf10(SUL2_bic,SUL1_bic) # null comes first, the results are for the null
bic_bf10(SUL3_bic,SUL1_bic) # convert BICs to BF
bic_bf10(SUL3_bic,SUL2_bic) # convert BICs to BF


```

